BackgroundThe ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning... Show moreBackgroundThe ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level.MethodsPrediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82).ResultsProteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists’ ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%).ConclusionsThis study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements. Show less
Kluiver, H. de; Jansen, R.; Penninx, B.W.J.H.; Giltay, E.J.; Schoevers, R.A.; Milaneschi, Y. 2023
Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous... Show moreDepression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an “atypical/energy-related” symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An ‘AES profile’ score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10-12), isoleucine (β = 0.13, p = 1.45*10-10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10-9) and saturated fatty acid levels (β = 0.09, p = 3.68*10-10), and lower high-density lipoproteins cholesterol (β = −0.07, p = 1.14*10-4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature—commonly linked to cardiometabolic disorders—associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression’s detrimental impact on health. Show less
Schaick, G. van; Hajjouti, N. el; Nicolardi, S.; Hartog, J. den; Jansen, R.; Hoeven, R. van der; ... ; Dominguez Vega, E. 2022
Xylanases are of great value in various industries, including paper, food, and biorefinery. Due to their biotechnological production, these enzymes can contain a variety of post-translational... Show moreXylanases are of great value in various industries, including paper, food, and biorefinery. Due to their biotechnological production, these enzymes can contain a variety of post-translational modifications, which may have a profound effect on protein function. Understanding the structure-function relationship can guide the development of products with optimal performance. We have developed a workflow for the structural and functional characterization of an endo-1,4-beta-xylanase (ENDO-I) produced by Aspergillus niger with and without applying thermal stress. This workflow relies on orthogonal native separation techniques to resolve proteoforms. Mass spectrometry and activity assays of separated proteoforms permitted the establishment of structure-function relationships. The separation conditions were focus on balancing efficient separation and protein functionality. We employed size exclusion chromatography (SEC) to separate ENDO-I from other co-expressed proteins. Charge variants were investigated with ion exchange chromatography (IEX) and revealed the presence of low abundant glycated variants in the temperature-stressed material. To obtain better insights into the effect on glycation on function, we enriched for these species using boronate affinity chromatography (BAC). The activity measurements showed lower activity of glycated species compared to the non-modified enzyme. Altogether, this workflow allowed in-depth structural and functional characterization of ENDO-I proteoforms. Show less
For a long time it has been thought that habitation and landscape organisation only changed significantly from the Roman Period onwards. However, many developments were already started long before... Show moreFor a long time it has been thought that habitation and landscape organisation only changed significantly from the Roman Period onwards. However, many developments were already started long before Julius Caesar's Roman armies arrived in the southern Netherlands. The Iron Age landscapes were ordered and structured, contrasting with the still open Bronze Age landscapes. Iron Age people inhabited the same places for generations. At the same time they structured their immediate environment and surroundings resulting in a sustainable organisation and arrangement of the landscape.Recent excavations and (micro-)regional archaeological studies into habitation and landscape organisation, among others in the north-eastern region of the province Noord-Brabant, show that relicts from the past strongly dictated the organisation and structuring of later landscapes. The past in the past formed a guideline (dutch: leidraad) for later (Iron Age) inhabitants.The past can also be a guideline for the design, protection and preservation of contemporary landscapes. This aligns with a trend in which archaeologists are explicitly seeking the connection with present society. Therefore this book ends with a plea for a transition of the Dutch archaeological system in which living heritage can also be a guideline for the present. Show less
Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans... Show moreTrait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.Analyses of expression profiles from whole blood of 31,684 individuals identify cis-expression quantitative trait loci (eQTL) effects for 88% of genes and trans-eQTL effects for 37% of trait-associated variants. Show less
Dongen, J. van; Hagenbeek, F.A.; Suderman, M.; Roetman, P.J.; Sugden, K.; Chiocchetti, A.G.; ... ; BIOS Consortium 2021
DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first... Show moreDNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 x 10(-7); Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits. Show less
Background DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes... Show moreBackground DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. Results By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g.,NFKBIE,CDCA7(L), andNLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. Conclusion We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation. Show less
Objectives: The present study examined associations between immunometabolic characteristics (IMCs) and depressive symptom profiles (DSPs) in probands with lifetime diagnoses of depression and/or... Show moreObjectives: The present study examined associations between immunometabolic characteristics (IMCs) and depressive symptom profiles (DSPs) in probands with lifetime diagnoses of depression and/or anxiety disorders and their siblings. Methods: Data were from the Netherlands Study of Depression and Anxiety, comprising 256 probands with lifetime diagnoses of depression and/or anxiety and their 380 siblings. Measured IMCs included blood pressure, waist circumference, and levels of glucose, triglycerides, HDL cholesterol, CRP, TNF-alpha and IL-6. DSPs included mood, cognitive, somatic and atypical-like profiles. We cross-sectionally examined whether DSPs were associated with IMCs within probands and within siblings, and whether DSPs were associated with IMCs between probands and siblings. Results: Within probands and within siblings, higher BMI and waist circumference were associated with higher somatic and atypical-like profiles. Other IMCs (IL-6, glucose and HDL cholesterol) were significantly related to DSPs either within probands or within siblings. DSPs and IMCs were not associated between probands and siblings. Conclusions: The results suggest that there is a familial component for each trait, but no common familial factors for the association between DSPs and IMCs. Alternative mechanisms, such as direct causal effects or non-shared environmental risk factors, may better fit these results. Show less
Rooij, J. van; Mandaviya, P.R.; Claringbould, A.; Felix, J.F.; Dongen, J. van; Jansen, R.; ... ; BIOS Consortium 2019
BackgroundA large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used... Show moreBackgroundA large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.ResultsWe tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n =similar to 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.ConclusionsResults were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results. Show less
Zoest, R.A. van; Law, M.; Sabin, C.A.; Vaartjes, I.; Valk, M. van der; Arends, J.E.; ... ; Elst-Laurijssen, D.H. 2019
Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential... Show moreDespite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D. Show less
The river area Maaskant and adjacent sand area of Oss, located ‘between’ the current course of the river Meuse and the city Oss, are among the most intensively researched regions in the Netherlands... Show moreThe river area Maaskant and adjacent sand area of Oss, located ‘between’ the current course of the river Meuse and the city Oss, are among the most intensively researched regions in the Netherlands. Extensive archaeological and palynological research provides ample opportunities for an interregional research of the occupation and vegetation history of both areas. This article describes the intertwinement between the Holocene river area and the adjacent Pleistocene sandy soils, to eventually get a first insight of the relation(s) between the inhabitants of both regions in late prehistoric and Early Roman period (3000 BC – 250 AD). Show less
